When “Tell the Truth” Isn’t the Best Strategy

If you run a small business and are bidding for ad space on platforms like Google and Meta, you don’t have teams of data scientists optimizing your moves. Behind the scenes, complex algorithms decide which ads appear and at what price, processing millions of transactions every second. How those decisions are made isn’t visible to most advertisers, who just rely on the platform’s advice: tell me how much you are willing to pay for this ad, and I will choose the best option for you.

“But in markets of this scale and complexity, that’s a promise no platform can actually keep,” said Saša Pekeč, the Peterjohn-Richards Distinguished Professor of Business Administration of Duke University’s Fuqua School of Business.

“If you are willing to pay $10 and your competitors $7, you win an auction that you would have won anyway by bidding $8,” he said. “You win the bid, but you might have overpaid.”

In a paper forthcoming in Information Systems Research, Pekeč and Fuqua PhD candidate Chenxi Xu show that in many of today’s large digital markets, simply reporting your true valuation is not optimal strategy. Instead, they propose an approach that protects smaller advertisers against worst-case outcomes and often improves their results.

Their message is especially relevant for smaller players who lack the resources to optimize their participation in such markets and compete with complex bidding algorithms. In fact, they may not need to, Xu said.

The limits of truthful bidding

In digital ad markets, millions of transactions happen every second. No human can process them; algorithms do the work.

Platforms often encourage advertisers to report their “true value”—the maximum they are willing to pay—and let automated bidding systems handle the rest.

But as Xu explains, the underlying economics make it practically impossible for the platforms to design a system that always does the best for every bidder. “Most large-scale markets cannot be designed in a way that guarantees that truthful reporting is optimal,” he said. So when platforms advise smaller bidders to “just tell us your maximum,” they are offering a workable simplification—not a theoretical guarantee.

An uneven playing field

Large advertisers often employ specialized teams of data scientists or outside firms to optimize their market performance. In digital advertising, for example,  demand-side platforms deploy sophisticated bidding strategies on behalf of their clients. Such data-driven, computationally heavy strategies also involve estimating competitors’ behavior and fine-tuning bids. Small businesses—plumbers, local retailers, independent contractors—typically do not have resources to be competitive.  They are told: enter your budget, set your maximum bid, and trust the algorithm.

Pekeč and Xu’s research challenges the idea that small bidders must remain passive bidders. Even without detailed knowledge of competitors’ behavior, they argue, small bidders can do better.

The result is strongest for bidders with focused goals—those seeking to win a specific ad slot, rather than managing a complex portfolio of placements.

Planning for the worst to perform at your best

The paper proposes a “robust optimization framework” to help small players improve their bidding process.

Rather than assuming how their competitors will behave, or what probability distribution their bids follow, the framework asks a simpler question: What is the worst reasonable price I might have to pay?

In their model, a bidder considers a plausible set of rival bids. A small business, for example, can see that competitors usually bid between a certain range on Google ads—or, in real estate context, that similar apartments list between $1700 and $2400 per month. 

Consider a business owner who expects that one extra customer would generate $100 in revenue, and each ad click converts at 10%. That makes an ad slot worth $10—the ceiling beyond which the ad erases its own profit. If competitors typically bid between $4-$7, the smart move is to bid closer to $7, not $10. Bidding your maximum wins the slot, but at risk of overpaying for it. 

The goal is not to win as often as possible, but to choose a bid that performs well under the most adverse plausible conditions.

Xu explained that in many common auction formats—including generalized first-price and second-price auctions—even a modest amount of “bid shading” below one’s maximum willingness to pay improves payoff relative to reporting truthfully. In many standard digital ad market settings, the pattern is consistent, the research shows.

Implications beyond advertising

Although digital ads provide the most common application, the implications extend to electricity markets, transportation networks, and other large-scale algorithmic marketplaces where information asymmetries favor well-resourced players.

Pekeč said that standard economics often assumes perfectly rational players with detailed beliefs about competitors. But small bidders rarely have such knowledge.

“We need to get away from the idea that the small guy has the full understanding of everybody’s valuations,” he said. 

For small businesses allocating digital advertising budgets, this means that relying entirely on automated recommendations may compress margins. Bidding slightly below your stated maximum can protect against overpaying, without requiring sophisticated modeling.

Rethinking “just tell the truth”

Platforms encourage truthful reporting for good reasons, Pekeč said. It reduces the strategizing burden that would otherwise keep many small participants on the sidelines. Wider participation, in turn, helps these markets deliver better value for everyone.

But the larger players are those who reap the benefits of strategic sophistication. Large advertisers offload their burden onto analysts and algorithms.

For small businesses, the research offers a practical alternative.

“If everybody else is following the platform-recommended advice of sharing their true value, there is an easy strategy: don't put that number. Put 10 cents less, or 10% less. It’s better than bidding your true value,” Xu said. 

It is a modest deviation—but one grounded in economic theory. And in markets measured in billions of transactions per day, small strategic shifts can compound.

The opportunity for better outcomes, Pekeč said, is simpler than small advertisers may assume. 

“What we’re saying is just move a little bit in the right direction. It’s always better than telling the truth to an opaque algorithm-driven system,” he said.